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Alan C. Bovik - One of the best experts on this subject based on the ideXlab platform.

  • referenceless prediction of Perceptual fog density and Perceptual Image defogging
    IEEE Transactions on Image Processing, 2015
    Co-Authors: Lark Kwon Choi, Jaehee You, Alan C. Bovik
    Abstract:

    We propose a referenceless Perceptual fog density prediction model based on natural scene statistics (NSS) and fog aware statistical features. The proposed model, called Fog Aware Density Evaluator (FADE), predicts the visibility of a foggy scene from a single Image without reference to a corresponding fog-free Image, without dependence on salient objects in a scene, without side geographical camera information, without estimating a depth-dependent transmission map, and without training on human-rated judgments. FADE only makes use of measurable deviations from statistical regularities observed in natural foggy and fog-free Images. Fog aware statistical features that define the Perceptual fog density index derive from a space domain NSS model and the observed characteristics of foggy Images. FADE not only predicts Perceptual fog density for the entire Image, but also provides a local fog density index for each patch. The predicted fog density using FADE correlates well with human judgments of fog density taken in a subjective study on a large foggy Image database. As applications, FADE not only accurately assesses the performance of defogging algorithms designed to enhance the visibility of foggy Images, but also is well suited for Image defogging. A new FADE-based referenceless Perceptual Image defogging, dubbed DEnsity of Fog Assessment-based DEfogger (DEFADE) achieves better results for darker, denser foggy Images as well as on standard foggy Images than the state of the art defogging methods. A software release of FADE and DEFADE is available online for public use: http://live.ece.utexas.edu/research/fog/index.html .

  • Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
    IEEE Transactions on Image Processing, 2014
    Co-Authors: Wufeng Xue, Lei Zhang, Xuanqin Mou, Alan C. Bovik
    Abstract:

    It is an important task to faithfully evaluate the Perceptual quality of output Images in many applications, such as Image compression, Image restoration, and multimedia streaming. A good Image quality assessment (IQA) model should not only deliver high quality prediction accuracy, but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The Image gradients are sensitive to Image distortions, while different local structures in a distorted Image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall Image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted Images combined with a novel pooling strategy-the standard deviation of the GMS map-can predict accurately Perceptual Image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy. MATLAB source code of GMSD can be downloaded at http://www4.comp.polyu.edu.hk/~cslzhang/IQA/GMSD/GMSD.htm.

  • Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
    arXiv: Computer Vision and Pattern Recognition, 2013
    Co-Authors: Wufeng Xue, Lei Zhang, Xuanqin Mou, Alan C. Bovik
    Abstract:

    It is an important task to faithfully evaluate the Perceptual quality of output Images in many applications such as Image compression, Image restoration and multimedia streaming. A good Image quality assessment (IQA) model should not only deliver high quality prediction accuracy but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The Image gradients are sensitive to Image distortions, while different local structures in a distorted Image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall Image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted Images combined with a novel pooling strategy the standard deviation of the GMS map can predict accurately Perceptual Image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy.

  • automatic prediction of Perceptual Image and video quality
    Proceedings of the IEEE, 2013
    Co-Authors: Alan C. Bovik
    Abstract:

    Finding ways to monitor and control the Perceptual quality of digital visual media has become a pressing concern as the volume being transported and viewed continues to increase exponentially. This paper discusses the principles and methods of modern algorithms for automatically predicting the quality of visual signals. By casting the problem as analogous to assessing the efficacy of a visual communication system, it is possible to divide the quality assessment problem into understandable modeling subproblems. Along the way, we will visit models of natural Images and videos, of visual perception, and a broad spectrum of applications.

  • Image Quality Assessment: From Error Visibility to Structural Similarity
    2004
    Co-Authors: Zhou Wang, Hamid Rahim Sheikh, Alan C. Bovik, Eero P Simoncelli
    Abstract:

    Objective methods for assessing Perceptual Image quality have traditionally attempted to quantify the visibility of errors between a distorted Image and a reference Image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of Images compressed with JPEG and JPEG2000

Thomas J. Bouchard - One of the best experts on this subject based on the ideXlab platform.

  • Genetic and environmental influences on the Verbal-Perceptual-Image Rotation (VPR) model of the structure of mental abilities in the Minnesota study of twins reared apart
    Intelligence, 2007
    Co-Authors: Wendy Johnson, Nancy L. Segal, Margaret Keyes, Matt Mcgue, Thomas J. Bouchard, Auke Tellegen, Irving I Gottesman
    Abstract:

    Abstract In previous papers [ Johnson, W., & Bouchard Jr., T. J. (2005a) . Constructive Replication of the Visual-Perceptual-Image Rotation (VPR) Model in Thurstone's (1941) Battery of 60 Tests of Mental Ability. Intelligence, 33, 417–430.] [ Johnson, W., & Bouchard Jr., T. J. (2005b) . The Structure of Human Intelligence: It's Verbal, Perceptual, and Image rotation (VPR), not Fluid and Crystallized. Intelligence, 33, 393–416.] we have proposed the Verbal, Perceptual, and Image rotation (VPR) model of the structure of mental abilities. The VPR model is hierarchical, with a g factor that contributes strongly to broad verbal, Perceptual, and Image rotation abilities, which in turn contribute to 8 more specialized abilities. The verbal and Perceptual abilities, though separable, are highly correlated, as are the Perceptual and mental rotation abilities. The verbal and mental rotation abilities are much less correlated. In this study we used the twin sample in the Minnesota Study of Twins Reared Apart to estimate the genetic and environmental influences and the correlations among them at each order of the VPR model. Genetic influences accounted for 67–79% of the variance throughout the model, with the exception of the second-stratum Content Memory factor, which showed 33% genetic influence. These influences could not be attributed to assessed similarity of rearing environment. Genetic correlations closely mirrored the phenotypic correlations. Together, these findings substantiate the theory that the entire structure of mental abilities is strongly influenced by genes.

  • Replication of the hierarchical visual-Perceptual-Image rotation model in de Wolff and Buiten's (1963) battery of 46 tests of mental ability
    Intelligence, 2006
    Co-Authors: Wendy Johnson, Jan Te Nijenhuis, Thomas J. Bouchard
    Abstract:

    Abstract In two recent papers, Johnson & Bouchard [Johnson, W., & Bouchard, T. J., Jr. (2005a). The structure of human intelligence: It is verbal, Perceptual, and Image rotation (VPR) , not fluid and crystallized. Intelligence, 33 , 393-416, Johnson W., & Bouchard, T. J., Jr. (2005b). Constructive replication of the visual Perceptual-Image rotation model in Thurstone's (1941) battery of 60 tests of mental ability. Intelligence, 33 , 417-430.] have evaluated the relative descriptive accuracies of the Cattell–Horn fluid-crystallized model and the Vernon verbal-Perceptual model of the structure of human intelligence. In all three samples evaluated in those papers, the Vernon model provided a measurably more accurate description of the data. In addition, descriptive accuracy could be significantly improved with the use of a four-stratum model with a g factor at the top of the hierarchy and three factors at the third stratum. The model accords similar importance to verbal, Perceptual, and Image rotation abilities in the hierarchy; thus, Johnson and Bouchard termed it the Verbal-Perceptual-Image Rotation (VPR) model. In the current study, we constructively replicated the model comparisons and development of the VPR model using the data matrix of 46 mental ability tests published by de Wolff and Buiten [de Wolff, Ch. J., & Buiten (1963). Een factoranalyse van vier testbatterijen [A factor analysis of four test batteries]. Nederlands Tijdschrift voor de Psychologie, 18 , 220-239.]. The sample consisted of 500 professional seamen of the Royal Dutch Navy.

  • constructive replication of the visual Perceptual Image rotation model in thurstone s 1941 battery of 60 tests of mental ability
    Intelligence, 2005
    Co-Authors: Wendy Johnson, Thomas J. Bouchard
    Abstract:

    Abstract We recently evaluated the relative statistical performance of the Cattell–Horn fluid–crystallized model and the Vernon verbal–Perceptual model of the structure of human intelligence in a sample of 436 adults heterogeneous for age, place of origin, and educational background who completed 42 separate tests of mental ability from three test batteries. We concluded that the Vernon model's performance was substantively superior but could be significantly improved. In so doing, we proposed a four-stratum model with a g factor at the top of the hierarchy and three factors at the third stratum. We termed this the Verbal–Perceptual-Image Rotation (VPR) model. In this study, we constructively replicated the model comparisons and development of the VPR model using the data matrix published by Thurstone and Thurstone (1941) [Thurstone, L. L., & Thurstone T. G. (1941). Factorial studies of intelligence. Chicago: University of Chicago Press]. The data matrix was generated by scores of 710 Chicago eighth graders on 60 tests of mental ability.

Wendy Johnson - One of the best experts on this subject based on the ideXlab platform.

  • Genetic and environmental influences on the Verbal-Perceptual-Image Rotation (VPR) model of the structure of mental abilities in the Minnesota study of twins reared apart
    Intelligence, 2007
    Co-Authors: Wendy Johnson, Nancy L. Segal, Margaret Keyes, Matt Mcgue, Thomas J. Bouchard, Auke Tellegen, Irving I Gottesman
    Abstract:

    Abstract In previous papers [ Johnson, W., & Bouchard Jr., T. J. (2005a) . Constructive Replication of the Visual-Perceptual-Image Rotation (VPR) Model in Thurstone's (1941) Battery of 60 Tests of Mental Ability. Intelligence, 33, 417–430.] [ Johnson, W., & Bouchard Jr., T. J. (2005b) . The Structure of Human Intelligence: It's Verbal, Perceptual, and Image rotation (VPR), not Fluid and Crystallized. Intelligence, 33, 393–416.] we have proposed the Verbal, Perceptual, and Image rotation (VPR) model of the structure of mental abilities. The VPR model is hierarchical, with a g factor that contributes strongly to broad verbal, Perceptual, and Image rotation abilities, which in turn contribute to 8 more specialized abilities. The verbal and Perceptual abilities, though separable, are highly correlated, as are the Perceptual and mental rotation abilities. The verbal and mental rotation abilities are much less correlated. In this study we used the twin sample in the Minnesota Study of Twins Reared Apart to estimate the genetic and environmental influences and the correlations among them at each order of the VPR model. Genetic influences accounted for 67–79% of the variance throughout the model, with the exception of the second-stratum Content Memory factor, which showed 33% genetic influence. These influences could not be attributed to assessed similarity of rearing environment. Genetic correlations closely mirrored the phenotypic correlations. Together, these findings substantiate the theory that the entire structure of mental abilities is strongly influenced by genes.

  • Replication of the hierarchical visual-Perceptual-Image rotation model in de Wolff and Buiten's (1963) battery of 46 tests of mental ability
    Intelligence, 2006
    Co-Authors: Wendy Johnson, Jan Te Nijenhuis, Thomas J. Bouchard
    Abstract:

    Abstract In two recent papers, Johnson & Bouchard [Johnson, W., & Bouchard, T. J., Jr. (2005a). The structure of human intelligence: It is verbal, Perceptual, and Image rotation (VPR) , not fluid and crystallized. Intelligence, 33 , 393-416, Johnson W., & Bouchard, T. J., Jr. (2005b). Constructive replication of the visual Perceptual-Image rotation model in Thurstone's (1941) battery of 60 tests of mental ability. Intelligence, 33 , 417-430.] have evaluated the relative descriptive accuracies of the Cattell–Horn fluid-crystallized model and the Vernon verbal-Perceptual model of the structure of human intelligence. In all three samples evaluated in those papers, the Vernon model provided a measurably more accurate description of the data. In addition, descriptive accuracy could be significantly improved with the use of a four-stratum model with a g factor at the top of the hierarchy and three factors at the third stratum. The model accords similar importance to verbal, Perceptual, and Image rotation abilities in the hierarchy; thus, Johnson and Bouchard termed it the Verbal-Perceptual-Image Rotation (VPR) model. In the current study, we constructively replicated the model comparisons and development of the VPR model using the data matrix of 46 mental ability tests published by de Wolff and Buiten [de Wolff, Ch. J., & Buiten (1963). Een factoranalyse van vier testbatterijen [A factor analysis of four test batteries]. Nederlands Tijdschrift voor de Psychologie, 18 , 220-239.]. The sample consisted of 500 professional seamen of the Royal Dutch Navy.

  • constructive replication of the visual Perceptual Image rotation model in thurstone s 1941 battery of 60 tests of mental ability
    Intelligence, 2005
    Co-Authors: Wendy Johnson, Thomas J. Bouchard
    Abstract:

    Abstract We recently evaluated the relative statistical performance of the Cattell–Horn fluid–crystallized model and the Vernon verbal–Perceptual model of the structure of human intelligence in a sample of 436 adults heterogeneous for age, place of origin, and educational background who completed 42 separate tests of mental ability from three test batteries. We concluded that the Vernon model's performance was substantively superior but could be significantly improved. In so doing, we proposed a four-stratum model with a g factor at the top of the hierarchy and three factors at the third stratum. We termed this the Verbal–Perceptual-Image Rotation (VPR) model. In this study, we constructively replicated the model comparisons and development of the VPR model using the data matrix published by Thurstone and Thurstone (1941) [Thurstone, L. L., & Thurstone T. G. (1941). Factorial studies of intelligence. Chicago: University of Chicago Press]. The data matrix was generated by scores of 710 Chicago eighth graders on 60 tests of mental ability.

Lei Zhang - One of the best experts on this subject based on the ideXlab platform.

  • multiscale contrast similarity deviation an effective and efficient index for Perceptual Image quality assessment
    Signal Processing-image Communication, 2016
    Co-Authors: Tonghan Wang, Lei Zhang, Huizhen Jia, Huazhong Shu
    Abstract:

    Perceptual Image quality assessment (IQA) uses a computational model to assess the Image quality in a fashion consistent with human opinions. A good IQA model should consider both the effectiveness and efficiency. To meet this need, a new model called multiscale contrast similarity deviation (MCSD) is developed in this paper. Contrast is a distinctive visual attribute closely related to the quality of an Image. To further explore the contrast features, we resort to the multiscale representation. Although the contrast and the multiscale representation have already been used by other IQA indices, few have reached the goals of effectiveness and efficiency simultaneously. We compared our method with other state-of-the-art methods using six well-known databases. The experimental results showed that the proposed method yielded the best performance in terms of correlation with human judgments. Furthermore, it is also efficient when compared with other competing IQA models.

  • Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
    IEEE Transactions on Image Processing, 2014
    Co-Authors: Wufeng Xue, Lei Zhang, Xuanqin Mou, Alan C. Bovik
    Abstract:

    It is an important task to faithfully evaluate the Perceptual quality of output Images in many applications, such as Image compression, Image restoration, and multimedia streaming. A good Image quality assessment (IQA) model should not only deliver high quality prediction accuracy, but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The Image gradients are sensitive to Image distortions, while different local structures in a distorted Image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall Image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted Images combined with a novel pooling strategy-the standard deviation of the GMS map-can predict accurately Perceptual Image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy. MATLAB source code of GMSD can be downloaded at http://www4.comp.polyu.edu.hk/~cslzhang/IQA/GMSD/GMSD.htm.

  • Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
    arXiv: Computer Vision and Pattern Recognition, 2013
    Co-Authors: Wufeng Xue, Lei Zhang, Xuanqin Mou, Alan C. Bovik
    Abstract:

    It is an important task to faithfully evaluate the Perceptual quality of output Images in many applications such as Image compression, Image restoration and multimedia streaming. A good Image quality assessment (IQA) model should not only deliver high quality prediction accuracy but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The Image gradients are sensitive to Image distortions, while different local structures in a distorted Image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall Image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted Images combined with a novel pooling strategy the standard deviation of the GMS map can predict accurately Perceptual Image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy.

Zhou Wang - One of the best experts on this subject based on the ideXlab platform.

  • information content weighting for Perceptual Image quality assessment
    IEEE Transactions on Image Processing, 2011
    Co-Authors: Zhou Wang
    Abstract:

    Many state-of-the-art Perceptual Image quality assessment (IQA) algorithms share a common two-stage structure: local quality/distortion measurement followed by pooling. While significant progress has been made in measuring local Image quality/distortion, the pooling stage is often done in ad-hoc ways, lacking theoretical principles and reliable computational models. This paper aims to test the hypothesis that when viewing natural Images, the optimal Perceptual weights for pooling should be proportional to local information content, which can be estimated in units of bit using advanced statistical models of natural Images. Our extensive studies based upon six publicly-available subject-rated Image databases concluded with three useful findings. First, information content weighting leads to consistent improvement in the performance of IQA algorithms. Second, surprisingly, with information content weighting, even the widely criticized peak signal-to-noise-ratio can be converted to a competitive Perceptual quality measure when compared with state-of-the-art algorithms. Third, the best overall performance is achieved by combining information content weighting with multiscale structural similarity measures.

  • spatial pooling strategies for Perceptual Image quality assessment
    International Conference on Image Processing, 2006
    Co-Authors: Zhou Wang, Xinli Shang
    Abstract:

    Many recently proposed Perceptual Image quality assessment algorithms are implemented in two stages. In the first stage, Image quality is evaluated within local regions. This results in a quality/distortion map over the Image space. In the second stage, a spatial pooling algorithm is employed that combines the quality/distortion map into a single quality score. While great effort has been devoted to developing algorithms for the first stage, little has been done to find the best strategies for the second stage (and simple spatial average is often used). In this work, we investigate three spatial pooling methods for the second stage: Minkowski pooling, local quality/distortion-weighted pooling, and information content-weighted pooling. Extensive experiments with the LIVE database show that all three methods may improve the prediction performance of Perceptual Image quality measures, but the third method demonstrates the best potential to be a general and robust method that leads to consistent improvement over a wide range of Image distortion types.

  • stimulus synthesis for efficient evaluation and refinement of Perceptual Image quality metrics
    Human Vision and Electronic Imaging Conference, 2004
    Co-Authors: Zhou Wang, Eero P Simoncelli
    Abstract:

    We propose a methodology for comparing and refining Perceptual Image quality metrics based on synthetic Images that are optimized to best differentiate two candidate quality metrics. We start from an initial distorted Image and iteratively search for the best/worst Images in terms of one metric while constraining the value of the other to remain fixed. We then repeat this, reversing the roles of the two metrics. Subjective test on the quality of pairs of these Images generated at different initial distortion levels provides a strong indication of the relative strength and weaknesses of the metrics being compared. This methodology also provides an efficient way to further refine the definition of an Image quality metric.

  • Image Quality Assessment: From Error Visibility to Structural Similarity
    2004
    Co-Authors: Zhou Wang, Hamid Rahim Sheikh, Eero P Simoncelli, Alan Conrad Bovik, Student Member, Senior Member
    Abstract:

    Objective methods for assessing Perceptual Image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted Image and a reference Image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of Images compressed with JPEG and JPEG2000

  • Image Quality Assessment: From Error Visibility to Structural Similarity
    2004
    Co-Authors: Zhou Wang, Hamid Rahim Sheikh, Alan C. Bovik, Eero P Simoncelli
    Abstract:

    Objective methods for assessing Perceptual Image quality have traditionally attempted to quantify the visibility of errors between a distorted Image and a reference Image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of Images compressed with JPEG and JPEG2000